Overview

Dataset statistics

Number of variables27
Number of observations27243
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.6 MiB
Average record size in memory216.0 B

Variable types

Numeric18
Categorical9

Alerts

FV_DATE has a high cardinality: 342 distinct values High cardinality
VISIT_CODE has a high cardinality: 27243 distinct values High cardinality
WP_NAME has a high cardinality: 18718 distinct values High cardinality
WP_ADDRESS has a high cardinality: 22266 distinct values High cardinality
NAICS.DESCR has a high cardinality: 636 distinct values High cardinality
NAICS is highly correlated with HIC_Rate and 1 other fieldsHigh correlation
Total_Claims is highly correlated with Fracture and 7 other fieldsHigh correlation
Fracture is highly correlated with Total_Claims and 6 other fieldsHigh correlation
Low_Back is highly correlated with Total_Claims and 7 other fieldsHigh correlation
Shoulder is highly correlated with Total_Claims and 7 other fieldsHigh correlation
HIC_Total is highly correlated with Total_Claims and 7 other fieldsHigh correlation
HIC_Rate is highly correlated with NAICS and 1 other fieldsHigh correlation
Emp_15plus is highly correlated with Total_Claims and 7 other fieldsHigh correlation
Emp_15.24 is highly correlated with Total_Claims and 8 other fieldsHigh correlation
EmpPer_1524 is highly correlated with Emp_15.24 and 1 other fieldsHigh correlation
Emp_25.54 is highly correlated with Total_Claims and 7 other fieldsHigh correlation
EmpPer_2554 is highly correlated with NAICS and 2 other fieldsHigh correlation
Emp_55plus is highly correlated with Total_Claims and 7 other fieldsHigh correlation
EmpPer_55plus is highly correlated with EmpPer_1524High correlation
NAICS is highly correlated with HIC_RateHigh correlation
Total_Claims is highly correlated with Fracture and 8 other fieldsHigh correlation
Fracture is highly correlated with Total_Claims and 3 other fieldsHigh correlation
Low_Back is highly correlated with Total_Claims and 8 other fieldsHigh correlation
Shoulder is highly correlated with Total_Claims and 7 other fieldsHigh correlation
HIC_Total is highly correlated with Total_Claims and 7 other fieldsHigh correlation
HIC_Rate is highly correlated with NAICS and 6 other fieldsHigh correlation
Emp_15plus is highly correlated with Total_Claims and 7 other fieldsHigh correlation
Emp_15.24 is highly correlated with Total_Claims and 9 other fieldsHigh correlation
EmpPer_1524 is highly correlated with Emp_15.24 and 2 other fieldsHigh correlation
Emp_25.54 is highly correlated with Total_Claims and 7 other fieldsHigh correlation
EmpPer_2554 is highly correlated with Emp_15.24 and 2 other fieldsHigh correlation
Emp_55plus is highly correlated with Total_Claims and 7 other fieldsHigh correlation
EmpPer_55plus is highly correlated with EmpPer_1524 and 1 other fieldsHigh correlation
NAICS is highly correlated with EmpPer_2554High correlation
Total_Claims is highly correlated with Fracture and 6 other fieldsHigh correlation
Fracture is highly correlated with Total_Claims and 3 other fieldsHigh correlation
Low_Back is highly correlated with Total_Claims and 6 other fieldsHigh correlation
Shoulder is highly correlated with Total_Claims and 6 other fieldsHigh correlation
HIC_Total is highly correlated with Total_Claims and 6 other fieldsHigh correlation
Emp_15plus is highly correlated with Total_Claims and 6 other fieldsHigh correlation
Emp_15.24 is highly correlated with Emp_15plus and 4 other fieldsHigh correlation
EmpPer_1524 is highly correlated with Emp_15.24 and 1 other fieldsHigh correlation
Emp_25.54 is highly correlated with Total_Claims and 6 other fieldsHigh correlation
EmpPer_2554 is highly correlated with NAICS and 1 other fieldsHigh correlation
Emp_55plus is highly correlated with Total_Claims and 6 other fieldsHigh correlation
EmpPer_55plus is highly correlated with EmpPer_1524High correlation
IND is highly correlated with IND_Cat and 1 other fieldsHigh correlation
IND_Cat is highly correlated with IND and 1 other fieldsHigh correlation
IND_Name is highly correlated with IND and 1 other fieldsHigh correlation
WP_ID is highly correlated with IND_Name and 2 other fieldsHigh correlation
REGION_CODE is highly correlated with Total_Claims and 4 other fieldsHigh correlation
IND_Name is highly correlated with WP_ID and 16 other fieldsHigh correlation
IND_Cat is highly correlated with IND_Name and 15 other fieldsHigh correlation
IND is highly correlated with WP_ID and 16 other fieldsHigh correlation
NAICS is highly correlated with IND_Name and 15 other fieldsHigh correlation
Total_Claims is highly correlated with REGION_CODE and 16 other fieldsHigh correlation
Fracture is highly correlated with REGION_CODE and 16 other fieldsHigh correlation
Low_Back is highly correlated with REGION_CODE and 16 other fieldsHigh correlation
Shoulder is highly correlated with REGION_CODE and 16 other fieldsHigh correlation
HIC_Total is highly correlated with REGION_CODE and 16 other fieldsHigh correlation
HIC_Rate is highly correlated with IND_Name and 15 other fieldsHigh correlation
Emp_15plus is highly correlated with IND_Name and 15 other fieldsHigh correlation
Emp_15.24 is highly correlated with IND_Name and 15 other fieldsHigh correlation
EmpPer_1524 is highly correlated with WP_ID and 16 other fieldsHigh correlation
Emp_25.54 is highly correlated with IND_Name and 15 other fieldsHigh correlation
EmpPer_2554 is highly correlated with IND_Name and 15 other fieldsHigh correlation
Emp_55plus is highly correlated with IND_Name and 15 other fieldsHigh correlation
EmpPer_55plus is highly correlated with IND_Name and 15 other fieldsHigh correlation
VISIT_CODE is uniformly distributed Uniform
WP_ADDRESS is uniformly distributed Uniform
VISIT_CODE has unique values Unique
CONTRA_Total has 9353 (34.3%) zeros Zeros

Reproduction

Analysis started2022-07-24 15:36:08.781044
Analysis finished2022-07-24 15:37:32.092418
Duration1 minute and 23.31 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

FV_D
Real number (ℝ≥0)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.93583673
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size213.0 KiB
2022-07-24T11:37:32.220913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q19
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.509718492
Coefficient of variation (CV)0.5339988503
Kurtosis-1.150205379
Mean15.93583673
Median Absolute Deviation (MAD)7
Skewness0.01527951319
Sum434140
Variance72.41530881
MonotonicityNot monotonic
2022-07-24T11:37:32.421740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
161050
 
3.9%
91048
 
3.8%
191030
 
3.8%
15997
 
3.7%
25984
 
3.6%
17975
 
3.6%
18972
 
3.6%
11969
 
3.6%
8957
 
3.5%
5949
 
3.5%
Other values (21)17312
63.5%
ValueCountFrequency (%)
1557
2.0%
2683
2.5%
3791
2.9%
4931
3.4%
5949
3.5%
6816
3.0%
7897
3.3%
8957
3.5%
91048
3.8%
10919
3.4%
ValueCountFrequency (%)
31399
1.5%
30914
3.4%
29827
3.0%
28833
3.1%
27776
2.8%
26948
3.5%
25984
3.6%
24923
3.4%
23903
3.3%
22823
3.0%

FV_M
Real number (ℝ≥0)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.455309621
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size213.0 KiB
2022-07-24T11:37:32.637040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.256311686
Coefficient of variation (CV)0.5044392721
Kurtosis-1.102141138
Mean6.455309621
Median Absolute Deviation (MAD)3
Skewness-0.05010313367
Sum175862
Variance10.6035658
MonotonicityNot monotonic
2022-07-24T11:37:32.814012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
72787
10.2%
52566
9.4%
92560
9.4%
62556
9.4%
102498
9.2%
82365
8.7%
32232
8.2%
112124
7.8%
12121
7.8%
42054
7.5%
Other values (2)3380
12.4%
ValueCountFrequency (%)
12121
7.8%
21971
7.2%
32232
8.2%
42054
7.5%
52566
9.4%
62556
9.4%
72787
10.2%
82365
8.7%
92560
9.4%
102498
9.2%
ValueCountFrequency (%)
121409
5.2%
112124
7.8%
102498
9.2%
92560
9.4%
82365
8.7%
72787
10.2%
62556
9.4%
52566
9.4%
42054
7.5%
32232
8.2%

FV_DATE
Categorical

HIGH CARDINALITY

Distinct342
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size213.0 KiB
2019-06-19
 
198
2019-07-24
 
169
2019-05-15
 
167
2019-07-23
 
159
2019-05-30
 
158
Other values (337)
26392 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters272430
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row2019-10-23
2nd row2019-01-08
3rd row2019-01-16
4th row2019-01-23
5th row2019-02-04

Common Values

ValueCountFrequency (%)
2019-06-19198
 
0.7%
2019-07-24169
 
0.6%
2019-05-15167
 
0.6%
2019-07-23159
 
0.6%
2019-05-30158
 
0.6%
2019-09-18156
 
0.6%
2019-06-18153
 
0.6%
2019-05-29153
 
0.6%
2019-06-12152
 
0.6%
2019-05-08152
 
0.6%
Other values (332)25626
94.1%

Length

2022-07-24T11:37:33.008355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2019-06-19198
 
0.7%
2019-07-24169
 
0.6%
2019-05-15167
 
0.6%
2019-07-23159
 
0.6%
2019-05-30158
 
0.6%
2019-09-18156
 
0.6%
2019-06-18153
 
0.6%
2019-05-29153
 
0.6%
2019-06-12152
 
0.6%
2019-05-08152
 
0.6%
Other values (332)25626
94.1%

Most occurring characters

ValueCountFrequency (%)
061171
22.5%
-54486
20.0%
149972
18.3%
241781
15.3%
932708
12.0%
36096
 
2.2%
55496
 
2.0%
75435
 
2.0%
65370
 
2.0%
85127
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number217944
80.0%
Dash Punctuation54486
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
061171
28.1%
149972
22.9%
241781
19.2%
932708
15.0%
36096
 
2.8%
55496
 
2.5%
75435
 
2.5%
65370
 
2.5%
85127
 
2.4%
44788
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
-54486
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common272430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
061171
22.5%
-54486
20.0%
149972
18.3%
241781
15.3%
932708
12.0%
36096
 
2.2%
55496
 
2.0%
75435
 
2.0%
65370
 
2.0%
85127
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII272430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
061171
22.5%
-54486
20.0%
149972
18.3%
241781
15.3%
932708
12.0%
36096
 
2.2%
55496
 
2.0%
75435
 
2.0%
65370
 
2.0%
85127
 
1.9%

VISIT_CODE
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct27243
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size213.0 KiB
2019-10-23 2123905
 
1
2019-11-13 2101587
 
1
2019-01-22 2056309
 
1
2019-12-06 2017472
 
1
2019-07-04 2017472
 
1
Other values (27238)
27238 

Length

Max length18
Median length18
Mean length17.84509782
Min length13

Characters and Unicode

Total characters486154
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27243 ?
Unique (%)100.0%

Sample

1st row2019-10-23 2123905
2nd row2019-01-08 2012482
3rd row2019-01-16 2055708
4th row2019-01-23 2056530
5th row2019-02-04 2058458

Common Values

ValueCountFrequency (%)
2019-10-23 21239051
 
< 0.1%
2019-11-13 21015871
 
< 0.1%
2019-01-22 20563091
 
< 0.1%
2019-12-06 20174721
 
< 0.1%
2019-07-04 20174721
 
< 0.1%
2019-02-01 20580521
 
< 0.1%
2019-01-22 20563631
 
< 0.1%
2019-01-26 19954231
 
< 0.1%
2019-12-24 20955161
 
< 0.1%
2019-07-04 20508041
 
< 0.1%
Other values (27233)27233
> 99.9%

Length

2022-07-24T11:37:33.196570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2019-06-19198
 
0.4%
2019-07-24169
 
0.3%
2019-05-15167
 
0.3%
2019-07-23159
 
0.3%
2019-05-30158
 
0.3%
2019-09-18156
 
0.3%
2019-06-18153
 
0.3%
2019-05-29153
 
0.3%
2019-05-08152
 
0.3%
2019-07-16152
 
0.3%
Other values (24041)52869
97.0%

Most occurring characters

ValueCountFrequency (%)
089305
18.4%
275664
15.6%
174959
15.4%
-54486
11.2%
948373
10.0%
27243
 
5.6%
320143
 
4.1%
820121
 
4.1%
719800
 
4.1%
519351
 
4.0%
Other values (2)36709
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number404425
83.2%
Dash Punctuation54486
 
11.2%
Space Separator27243
 
5.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
089305
22.1%
275664
18.7%
174959
18.5%
948373
12.0%
320143
 
5.0%
820121
 
5.0%
719800
 
4.9%
519351
 
4.8%
618894
 
4.7%
417815
 
4.4%
Dash Punctuation
ValueCountFrequency (%)
-54486
100.0%
Space Separator
ValueCountFrequency (%)
27243
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common486154
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
089305
18.4%
275664
15.6%
174959
15.4%
-54486
11.2%
948373
10.0%
27243
 
5.6%
320143
 
4.1%
820121
 
4.1%
719800
 
4.1%
519351
 
4.0%
Other values (2)36709
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII486154
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
089305
18.4%
275664
15.6%
174959
15.4%
-54486
11.2%
948373
10.0%
27243
 
5.6%
320143
 
4.1%
820121
 
4.1%
719800
 
4.1%
519351
 
4.0%
Other values (2)36709
7.6%

WP_ID
Real number (ℝ≥0)

HIGH CORRELATION

Distinct23709
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1822659.484
Minimum27
Maximum2137200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size213.0 KiB
2022-07-24T11:37:33.397264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile89608.1
Q11969155.5
median2064379
Q32095160
95-th percentile2125186.9
Maximum2137200
Range2137173
Interquartile range (IQR)126004.5

Descriptive statistics

Standard deviation575972.1205
Coefficient of variation (CV)0.316006432
Kurtosis3.869230398
Mean1822659.484
Median Absolute Deviation (MAD)38485
Skewness-2.292098749
Sum4.965471233 × 1010
Variance3.317438836 × 1011
MonotonicityNot monotonic
2022-07-24T11:37:33.645679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8517013
 
< 0.1%
20448839
 
< 0.1%
20376118
 
< 0.1%
19555368
 
< 0.1%
281968
 
< 0.1%
20265458
 
< 0.1%
940998
 
< 0.1%
20566717
 
< 0.1%
960207
 
< 0.1%
20571157
 
< 0.1%
Other values (23699)27160
99.7%
ValueCountFrequency (%)
271
< 0.1%
1361
< 0.1%
1601
< 0.1%
1611
< 0.1%
1872
< 0.1%
2611
< 0.1%
3991
< 0.1%
4971
< 0.1%
5341
< 0.1%
9631
< 0.1%
ValueCountFrequency (%)
21372001
< 0.1%
21365541
< 0.1%
21365151
< 0.1%
21365141
< 0.1%
21364861
< 0.1%
21364031
< 0.1%
21363911
< 0.1%
21363731
< 0.1%
21363691
< 0.1%
21363601
< 0.1%

WP_NAME
Categorical

HIGH CARDINALITY

Distinct18718
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Memory size213.0 KiB
ELLISDON CORPORATION
 
59
MATTAMY 2000 INC
 
34
SHOPPERS DRUG MART
 
33
Tim Hortons
 
31
Melloul Blamey Construction Inc
 
29
Other values (18713)
27057 

Length

Max length209
Median length68
Mean length22.16356495
Min length1

Characters and Unicode

Total characters603802
Distinct characters63
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15183 ?
Unique (%)55.7%

Sample

1st rowSolutions CMD
2nd rowD S CONSTRUCTION
3rd rowForesterie Malloy Inc
4th rowDEBUS DESIGN BUILD
5th rowLANG CONTRACTING

Common Values

ValueCountFrequency (%)
ELLISDON CORPORATION59
 
0.2%
MATTAMY 2000 INC34
 
0.1%
SHOPPERS DRUG MART33
 
0.1%
Tim Hortons31
 
0.1%
Melloul Blamey Construction Inc 29
 
0.1%
PARK VIEW HOMES29
 
0.1%
Fortis Construction Group Inc 28
 
0.1%
PCL Constructors Canada Inc 27
 
0.1%
PCL CONSTRUCTORS CANADA INC 26
 
0.1%
R K Porter General Contracting Inc 26
 
0.1%
Other values (18708)26921
98.8%

Length

2022-07-24T11:37:33.919367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inc5480
 
6.2%
construction3784
 
4.3%
ltd3397
 
3.8%
homes1971
 
2.2%
limited1581
 
1.8%
roofing1031
 
1.2%
s944
 
1.1%
contracting921
 
1.0%
and731
 
0.8%
group681
 
0.8%
Other values (14588)68478
76.9%

Most occurring characters

ValueCountFrequency (%)
76709
 
12.7%
n27060
 
4.5%
o25311
 
4.2%
e24896
 
4.1%
I21979
 
3.6%
i21788
 
3.6%
C21746
 
3.6%
t21659
 
3.6%
a21365
 
3.5%
N21072
 
3.5%
Other values (53)320217
53.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter265503
44.0%
Lowercase Letter253799
42.0%
Space Separator76709
 
12.7%
Decimal Number7791
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n27060
10.7%
o25311
10.0%
e24896
9.8%
i21788
8.6%
t21659
8.5%
a21365
8.4%
r20703
 
8.2%
s14942
 
5.9%
c11628
 
4.6%
l11415
 
4.5%
Other values (16)53032
20.9%
Uppercase Letter
ValueCountFrequency (%)
I21979
 
8.3%
C21746
 
8.2%
N21072
 
7.9%
T19838
 
7.5%
E19437
 
7.3%
R19294
 
7.3%
O19210
 
7.2%
A18170
 
6.8%
S16710
 
6.3%
L15204
 
5.7%
Other values (16)72843
27.4%
Decimal Number
ValueCountFrequency (%)
11154
14.8%
21109
14.2%
01035
13.3%
3705
9.0%
4696
8.9%
6673
8.6%
5672
8.6%
9670
8.6%
8576
7.4%
7501
6.4%
Space Separator
ValueCountFrequency (%)
76709
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin519302
86.0%
Common84500
 
14.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n27060
 
5.2%
o25311
 
4.9%
e24896
 
4.8%
I21979
 
4.2%
i21788
 
4.2%
C21746
 
4.2%
t21659
 
4.2%
a21365
 
4.1%
N21072
 
4.1%
r20703
 
4.0%
Other values (42)291723
56.2%
Common
ValueCountFrequency (%)
76709
90.8%
11154
 
1.4%
21109
 
1.3%
01035
 
1.2%
3705
 
0.8%
4696
 
0.8%
6673
 
0.8%
5672
 
0.8%
9670
 
0.8%
8576
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII603802
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
76709
 
12.7%
n27060
 
4.5%
o25311
 
4.2%
e24896
 
4.1%
I21979
 
3.6%
i21788
 
3.6%
C21746
 
3.6%
t21659
 
3.6%
a21365
 
3.5%
N21072
 
3.5%
Other values (53)320217
53.0%

WP_ADDRESS
Categorical

HIGH CARDINALITY
UNIFORM

Distinct22266
Distinct (%)81.7%
Missing0
Missing (%)0.0%
Memory size213.0 KiB
10 Nova Scotia Avenue (English), Toronto - Toronto - Toronto, ON, CANADA M6K 3C3
 
29
1 Bass Pro Mills Drive, Vaughan - York - Vaughan, ON, CANADA L4K 5W4
 
22
2370 Midland Avenue (English), Scarborough - Toronto - Toronto, ON, CANADA M1S 5C6
 
18
945 GARDINERS Road, Kingston - Frontenac - Kingston, ON, CANADA K7M 7H4
 
14
912 PINE Street South, TIMMINS, ON, CANADA P4N 2M3
 
13
Other values (22261)
27147 

Length

Max length178
Median length156
Mean length75.39393606
Min length28

Characters and Unicode

Total characters2053957
Distinct characters86
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18966 ?
Unique (%)69.6%

Sample

1st row275 Daniel Street, Arnprior - Renfrew - Arnprior, ON, CANADA A0A 1Z0
2nd rowListowel - Perth - North Perth, ON, CANADA LOT 17, 12, 14, 16, 29, 30 off of Kincard St
3rd row132 bisson Rang, Sherbrooke Township - Haldimand-Norfolk - Haldimand County, QC, CANADA J0V 1Y0
4th row199, Stratford - Perth - Stratford, ON, CANADA 199 CAMBRIA STREET
5th rowSt. Marys - Perth - St. Marys, ON, CANADA 74 WILSON CRT

Common Values

ValueCountFrequency (%)
10 Nova Scotia Avenue (English), Toronto - Toronto - Toronto, ON, CANADA M6K 3C329
 
0.1%
1 Bass Pro Mills Drive, Vaughan - York - Vaughan, ON, CANADA L4K 5W422
 
0.1%
2370 Midland Avenue (English), Scarborough - Toronto - Toronto, ON, CANADA M1S 5C618
 
0.1%
945 GARDINERS Road, Kingston - Frontenac - Kingston, ON, CANADA K7M 7H414
 
0.1%
912 PINE Street South, TIMMINS, ON, CANADA P4N 2M313
 
< 0.1%
500 College Street East, Belleville - Hastings - Belleville, ON, CANADA K8N 4Z613
 
< 0.1%
6301 Silver Dart Drive, Mississauga - Peel - Mississauga, ON, CANADA L5P 1B212
 
< 0.1%
1800 Sheppard Avenue (English) East, Toronto - Toronto - Toronto, ON, CANADA M2J 5A712
 
< 0.1%
1 BASS PRO MILLS Drive, Concord - York - Vaughan, ON, CANADA L4K 5W412
 
< 0.1%
1355 Kingston Road, Pickering - Durham - Pickering, ON, CANADA L1V 1B811
 
< 0.1%
Other values (22256)27087
99.4%

Length

2022-07-24T11:37:34.202280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
53529
 
14.7%
on27311
 
7.5%
canada27154
 
7.5%
street7533
 
2.1%
road7282
 
2.0%
toronto7210
 
2.0%
ottawa4944
 
1.4%
english4169
 
1.1%
drive3533
 
1.0%
mississauga3378
 
0.9%
Other values (15002)217998
59.9%

Most occurring characters

ValueCountFrequency (%)
337372
 
16.4%
A98190
 
4.8%
e94162
 
4.6%
a91080
 
4.4%
o87898
 
4.3%
,82903
 
4.0%
r81694
 
4.0%
t80146
 
3.9%
N76554
 
3.7%
n69145
 
3.4%
Other values (76)954813
46.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter861845
42.0%
Uppercase Letter532855
25.9%
Space Separator337390
 
16.4%
Decimal Number170641
 
8.3%
Other Punctuation87016
 
4.2%
Dash Punctuation55290
 
2.7%
Open Punctuation4461
 
0.2%
Close Punctuation4452
 
0.2%
Math Symbol4
 
< 0.1%
Modifier Symbol2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A98190
18.4%
N76554
14.4%
C42503
 
8.0%
O40839
 
7.7%
D36848
 
6.9%
S26011
 
4.9%
L23751
 
4.5%
E19550
 
3.7%
R19482
 
3.7%
T18623
 
3.5%
Other values (17)130504
24.5%
Lowercase Letter
ValueCountFrequency (%)
e94162
10.9%
a91080
10.6%
o87898
10.2%
r81694
9.5%
t80146
9.3%
n69145
 
8.0%
i54349
 
6.3%
l45434
 
5.3%
s44267
 
5.1%
d32352
 
3.8%
Other values (17)181318
21.0%
Other Punctuation
ValueCountFrequency (%)
,82903
95.3%
.2356
 
2.7%
/834
 
1.0%
&502
 
0.6%
'218
 
0.3%
#140
 
0.2%
;28
 
< 0.1%
:13
 
< 0.1%
"13
 
< 0.1%
@8
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
130601
17.9%
023529
13.8%
220066
11.8%
517312
10.1%
316899
9.9%
415750
9.2%
612895
7.6%
712383
7.3%
910981
 
6.4%
810225
 
6.0%
Space Separator
ValueCountFrequency (%)
337372
> 99.9%
 18
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
(4459
> 99.9%
{2
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
)4450
> 99.9%
}2
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
|3
75.0%
+1
 
25.0%
Dash Punctuation
ValueCountFrequency (%)
-55290
100.0%
Modifier Symbol
ValueCountFrequency (%)
`2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1394700
67.9%
Common659257
32.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A98190
 
7.0%
e94162
 
6.8%
a91080
 
6.5%
o87898
 
6.3%
r81694
 
5.9%
t80146
 
5.7%
N76554
 
5.5%
n69145
 
5.0%
i54349
 
3.9%
l45434
 
3.3%
Other values (44)616048
44.2%
Common
ValueCountFrequency (%)
337372
51.2%
,82903
 
12.6%
-55290
 
8.4%
130601
 
4.6%
023529
 
3.6%
220066
 
3.0%
517312
 
2.6%
316899
 
2.6%
415750
 
2.4%
612895
 
2.0%
Other values (22)46640
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2053932
> 99.9%
None25
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
337372
 
16.4%
A98190
 
4.8%
e94162
 
4.6%
a91080
 
4.4%
o87898
 
4.3%
,82903
 
4.0%
r81694
 
4.0%
t80146
 
3.9%
N76554
 
3.7%
n69145
 
3.4%
Other values (73)954788
46.5%
None
ValueCountFrequency (%)
 18
72.0%
é6
 
24.0%
É1
 
4.0%

REGION_CODE
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size213.0 KiB
L
9755 
N
6754 
K
4889 
P
3110 
M
2735 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters27243
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowK
2nd rowK
3rd rowK
4th rowK
5th rowK

Common Values

ValueCountFrequency (%)
L9755
35.8%
N6754
24.8%
K4889
17.9%
P3110
 
11.4%
M2735
 
10.0%

Length

2022-07-24T11:37:34.397718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-24T11:37:34.640336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
l9755
35.8%
n6754
24.8%
k4889
17.9%
p3110
 
11.4%
m2735
 
10.0%

Most occurring characters

ValueCountFrequency (%)
L9755
35.8%
N6754
24.8%
K4889
17.9%
P3110
 
11.4%
M2735
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter27243
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L9755
35.8%
N6754
24.8%
K4889
17.9%
P3110
 
11.4%
M2735
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Latin27243
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L9755
35.8%
N6754
24.8%
K4889
17.9%
P3110
 
11.4%
M2735
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII27243
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L9755
35.8%
N6754
24.8%
K4889
17.9%
P3110
 
11.4%
M2735
 
10.0%

IND_Name
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size213.0 KiB
Construction
16731 
Service
3611 
Other services (except public administration)
 
1662
Manufacturing
 
1628
Accommodation and food services
 
1121
Other values (7)
2490 

Length

Max length45
Median length12
Mean length16.3952575
Min length7

Characters and Unicode

Total characters446656
Distinct characters37
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConstruction
2nd rowConstruction
3rd rowAgriculture, forestry, fishing and hunting
4th rowConstruction
5th rowConstruction

Common Values

ValueCountFrequency (%)
Construction16731
61.4%
Service3611
 
13.3%
Other services (except public administration)1662
 
6.1%
Manufacturing1628
 
6.0%
Accommodation and food services1121
 
4.1%
Mining, quarrying, and oil and gas extraction979
 
3.6%
Health care and social assistance818
 
3.0%
Transportation and warehousing279
 
1.0%
Public administration168
 
0.6%
Agriculture, forestry, fishing and hunting118
 
0.4%
Other values (2)128
 
0.5%

Length

2022-07-24T11:37:34.824965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
construction16731
35.1%
and4294
 
9.0%
service3611
 
7.6%
services2860
 
6.0%
public1830
 
3.8%
administration1830
 
3.8%
other1662
 
3.5%
except1662
 
3.5%
manufacturing1628
 
3.4%
accommodation1121
 
2.4%
Other values (18)10446
21.9%

Most occurring characters

ValueCountFrequency (%)
n51795
11.6%
t47880
10.7%
o44705
10.0%
i41062
9.2%
c34192
 
7.7%
r33386
 
7.5%
s29377
 
6.6%
u23506
 
5.3%
e21927
 
4.9%
20432
 
4.6%
Other values (27)98394
22.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter393463
88.1%
Uppercase Letter27243
 
6.1%
Space Separator20432
 
4.6%
Other Punctuation2194
 
0.5%
Close Punctuation1662
 
0.4%
Open Punctuation1662
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n51795
13.2%
t47880
12.2%
o44705
11.4%
i41062
10.4%
c34192
8.7%
r33386
8.5%
s29377
7.5%
u23506
6.0%
e21927
5.6%
a20349
 
5.2%
Other values (13)45284
11.5%
Uppercase Letter
ValueCountFrequency (%)
C16731
61.4%
S3611
 
13.3%
M2607
 
9.6%
O1662
 
6.1%
A1239
 
4.5%
H818
 
3.0%
T279
 
1.0%
P168
 
0.6%
E77
 
0.3%
U51
 
0.2%
Space Separator
ValueCountFrequency (%)
20432
100.0%
Other Punctuation
ValueCountFrequency (%)
,2194
100.0%
Close Punctuation
ValueCountFrequency (%)
)1662
100.0%
Open Punctuation
ValueCountFrequency (%)
(1662
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin420706
94.2%
Common25950
 
5.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
n51795
12.3%
t47880
11.4%
o44705
10.6%
i41062
9.8%
c34192
8.1%
r33386
7.9%
s29377
7.0%
u23506
 
5.6%
e21927
 
5.2%
a20349
 
4.8%
Other values (23)72527
17.2%
Common
ValueCountFrequency (%)
20432
78.7%
,2194
 
8.5%
)1662
 
6.4%
(1662
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII446656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n51795
11.6%
t47880
10.7%
o44705
10.0%
i41062
9.2%
c34192
 
7.7%
r33386
 
7.5%
s29377
 
6.6%
u23506
 
5.3%
e21927
 
4.9%
20432
 
4.6%
Other values (27)98394
22.0%

IND_Cat
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size213.0 KiB
Goods-producing
19507 
Service-producing
7736 

Length

Max length17
Median length15
Mean length15.56792571
Min length15

Characters and Unicode

Total characters424117
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGoods-producing
2nd rowGoods-producing
3rd rowGoods-producing
4th rowGoods-producing
5th rowGoods-producing

Common Values

ValueCountFrequency (%)
Goods-producing19507
71.6%
Service-producing7736
 
28.4%

Length

2022-07-24T11:37:35.022278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-24T11:37:35.250866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
goods-producing19507
71.6%
service-producing7736
 
28.4%

Most occurring characters

ValueCountFrequency (%)
o66257
15.6%
d46750
11.0%
r34979
8.2%
c34979
8.2%
i34979
8.2%
-27243
 
6.4%
p27243
 
6.4%
u27243
 
6.4%
n27243
 
6.4%
g27243
 
6.4%
Other values (5)69958
16.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter369631
87.2%
Dash Punctuation27243
 
6.4%
Uppercase Letter27243
 
6.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o66257
17.9%
d46750
12.6%
r34979
9.5%
c34979
9.5%
i34979
9.5%
p27243
7.4%
u27243
7.4%
n27243
7.4%
g27243
7.4%
s19507
 
5.3%
Other values (2)23208
 
6.3%
Uppercase Letter
ValueCountFrequency (%)
G19507
71.6%
S7736
 
28.4%
Dash Punctuation
ValueCountFrequency (%)
-27243
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin396874
93.6%
Common27243
 
6.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
o66257
16.7%
d46750
11.8%
r34979
8.8%
c34979
8.8%
i34979
8.8%
p27243
6.9%
u27243
6.9%
n27243
6.9%
g27243
6.9%
G19507
 
4.9%
Other values (4)50451
12.7%
Common
ValueCountFrequency (%)
-27243
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII424117
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o66257
15.6%
d46750
11.0%
r34979
8.2%
c34979
8.2%
i34979
8.2%
-27243
 
6.4%
p27243
 
6.4%
u27243
 
6.4%
n27243
 
6.4%
g27243
 
6.4%
Other values (5)69958
16.5%

IND
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size213.0 KiB
23
16731 
Multiple
3611 
81
 
1662
31-33
 
1628
72
 
1121
Other values (7)
2490 

Length

Max length8
Median length2
Mean length3.005285761
Min length2

Characters and Unicode

Total characters81873
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row23
2nd row23
3rd row11
4th row23
5th row23

Common Values

ValueCountFrequency (%)
2316731
61.4%
Multiple3611
 
13.3%
811662
 
6.1%
31-331628
 
6.0%
721121
 
4.1%
21979
 
3.6%
62818
 
3.0%
48-49279
 
1.0%
91168
 
0.6%
11118
 
0.4%
Other values (2)128
 
0.5%

Length

2022-07-24T11:37:35.432259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2316731
61.4%
multiple3611
 
13.3%
811662
 
6.1%
31-331628
 
6.0%
721121
 
4.1%
21979
 
3.6%
62818
 
3.0%
48-49279
 
1.0%
91168
 
0.6%
11118
 
0.4%
Other values (2)128
 
0.5%

Most occurring characters

ValueCountFrequency (%)
321615
26.4%
219751
24.1%
l7222
 
8.8%
14750
 
5.8%
M3611
 
4.4%
u3611
 
4.4%
t3611
 
4.4%
i3611
 
4.4%
p3611
 
4.4%
e3611
 
4.4%
Other values (6)6869
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number51078
62.4%
Lowercase Letter25277
30.9%
Uppercase Letter3611
 
4.4%
Dash Punctuation1907
 
2.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
321615
42.3%
219751
38.7%
14750
 
9.3%
81941
 
3.8%
71121
 
2.2%
6895
 
1.8%
4558
 
1.1%
9447
 
0.9%
Lowercase Letter
ValueCountFrequency (%)
l7222
28.6%
u3611
14.3%
t3611
14.3%
i3611
14.3%
p3611
14.3%
e3611
14.3%
Uppercase Letter
ValueCountFrequency (%)
M3611
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1907
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common52985
64.7%
Latin28888
35.3%

Most frequent character per script

Common
ValueCountFrequency (%)
321615
40.8%
219751
37.3%
14750
 
9.0%
81941
 
3.7%
-1907
 
3.6%
71121
 
2.1%
6895
 
1.7%
4558
 
1.1%
9447
 
0.8%
Latin
ValueCountFrequency (%)
l7222
25.0%
M3611
12.5%
u3611
12.5%
t3611
12.5%
i3611
12.5%
p3611
12.5%
e3611
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII81873
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
321615
26.4%
219751
24.1%
l7222
 
8.8%
14750
 
5.8%
M3611
 
4.4%
u3611
 
4.4%
t3611
 
4.4%
i3611
 
4.4%
p3611
 
4.4%
e3611
 
4.4%
Other values (6)6869
 
8.4%

NAICS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct636
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean346990.3042
Minimum111219
Maximum914110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size213.0 KiB
2022-07-24T11:37:35.657723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum111219
5-th percentile236110
Q1236110
median236220
Q3444110
95-th percentile812115
Maximum914110
Range802891
Interquartile range (IQR)208000

Descriptive statistics

Standard deviation187018.2076
Coefficient of variation (CV)0.5389724305
Kurtosis0.9132494721
Mean346990.3042
Median Absolute Deviation (MAD)900
Skewness1.499009755
Sum9453056858
Variance3.497580999 × 1010
MonotonicityNot monotonic
2022-07-24T11:37:35.932412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2361107827
28.7%
2362204347
16.0%
238160996
 
3.7%
812115933
 
3.4%
236210852
 
3.1%
237110594
 
2.2%
212323550
 
2.0%
722512527
 
1.9%
238990514
 
1.9%
237310491
 
1.8%
Other values (626)9612
35.3%
ValueCountFrequency (%)
1112191
 
< 0.1%
1113302
 
< 0.1%
1114125
< 0.1%
1114196
< 0.1%
1114211
 
< 0.1%
1114221
 
< 0.1%
1119101
 
< 0.1%
1119953
< 0.1%
1119991
 
< 0.1%
1121101
 
< 0.1%
ValueCountFrequency (%)
9141101
 
< 0.1%
913910133
0.5%
9131901
 
< 0.1%
9131503
 
< 0.1%
9131408
 
< 0.1%
9131301
 
< 0.1%
9131101
 
< 0.1%
91291012
 
< 0.1%
9121405
 
< 0.1%
9121301
 
< 0.1%

NAICS.DESCR
Categorical

HIGH CARDINALITY

Distinct636
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size213.0 KiB
Residential Building Construction
7827 
Commercial and Institutional Building Construction
4347 
Roofing Contractors
 
996
Beauty Salons
 
933
Industrial Building and Structure Construction
 
852
Other values (631)
12288 

Length

Max length108
Median length94
Mean length37.59068385
Min length7

Characters and Unicode

Total characters1024083
Distinct characters58
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique121 ?
Unique (%)0.4%

Sample

1st rowIndustrial Building and Structure Construction
2nd rowResidential Building Construction
3rd rowContract Logging
4th rowResidential Building Construction
5th rowResidential Building Construction

Common Values

ValueCountFrequency (%)
Residential Building Construction 7827
28.7%
Commercial and Institutional Building Construction 4347
16.0%
Roofing Contractors 996
 
3.7%
Beauty Salons 933
 
3.4%
Industrial Building and Structure Construction 852
 
3.1%
Water and Sewer Line and Related Structures Construction 594
 
2.2%
Sand and Gravel Mining and Quarrying 550
 
2.0%
Limited-service eating places527
 
1.9%
All Other Specialty Trade Contractors 514
 
1.9%
Highway, Street and Bridge Construction 491
 
1.8%
Other values (626)9612
35.3%

Length

2022-07-24T11:37:36.233816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
construction14615
 
13.0%
building13257
 
11.8%
and13022
 
11.6%
residential7963
 
7.1%
commercial4463
 
4.0%
institutional4349
 
3.9%
other3210
 
2.9%
contractors2222
 
2.0%
stores1980
 
1.8%
all1865
 
1.7%
Other values (892)45546
40.5%

Most occurring characters

ValueCountFrequency (%)
109345
 
10.7%
i99871
 
9.8%
n98673
 
9.6%
t85275
 
8.3%
e66544
 
6.5%
o61352
 
6.0%
a58866
 
5.7%
r56665
 
5.5%
l49220
 
4.8%
u47408
 
4.6%
Other values (48)290864
28.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter817148
79.8%
Space Separator109345
 
10.7%
Uppercase Letter93121
 
9.1%
Other Punctuation1675
 
0.2%
Dash Punctuation1330
 
0.1%
Close Punctuation732
 
0.1%
Open Punctuation732
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i99871
12.2%
n98673
12.1%
t85275
10.4%
e66544
8.1%
o61352
7.5%
a58866
7.2%
r56665
 
6.9%
l49220
 
6.0%
u47408
 
5.8%
s47347
 
5.8%
Other values (16)145927
17.9%
Uppercase Letter
ValueCountFrequency (%)
C24436
26.2%
B15321
16.5%
R10917
11.7%
S10176
10.9%
I5564
 
6.0%
M4044
 
4.3%
O3388
 
3.6%
A3100
 
3.3%
P3075
 
3.3%
L2053
 
2.2%
Other values (15)11047
11.9%
Other Punctuation
ValueCountFrequency (%)
,1532
91.5%
'133
 
7.9%
.10
 
0.6%
Space Separator
ValueCountFrequency (%)
109345
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1330
100.0%
Close Punctuation
ValueCountFrequency (%)
)732
100.0%
Open Punctuation
ValueCountFrequency (%)
(732
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin910269
88.9%
Common113814
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i99871
11.0%
n98673
10.8%
t85275
 
9.4%
e66544
 
7.3%
o61352
 
6.7%
a58866
 
6.5%
r56665
 
6.2%
l49220
 
5.4%
u47408
 
5.2%
s47347
 
5.2%
Other values (41)239048
26.3%
Common
ValueCountFrequency (%)
109345
96.1%
,1532
 
1.3%
-1330
 
1.2%
)732
 
0.6%
(732
 
0.6%
'133
 
0.1%
.10
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1024083
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
109345
 
10.7%
i99871
 
9.8%
n98673
 
9.6%
t85275
 
8.3%
e66544
 
6.5%
o61352
 
6.0%
a58866
 
5.7%
r56665
 
5.5%
l49220
 
4.8%
u47408
 
4.6%
Other values (48)290864
28.4%

CONTRA_Total
Real number (ℝ≥0)

ZEROS

Distinct39
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.625775429
Minimum0
Maximum46
Zeros9353
Zeros (%)34.3%
Negative0
Negative (%)0.0%
Memory size213.0 KiB
2022-07-24T11:37:36.486478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile9
Maximum46
Range46
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.301492458
Coefficient of variation (CV)1.257339993
Kurtosis9.763755324
Mean2.625775429
Median Absolute Deviation (MAD)2
Skewness2.298305909
Sum71534
Variance10.89985245
MonotonicityNot monotonic
2022-07-24T11:37:36.659633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
09353
34.3%
13777
13.9%
23631
 
13.3%
32706
 
9.9%
42190
 
8.0%
51469
 
5.4%
61145
 
4.2%
7767
 
2.8%
8586
 
2.2%
9412
 
1.5%
Other values (29)1207
 
4.4%
ValueCountFrequency (%)
09353
34.3%
13777
13.9%
23631
 
13.3%
32706
 
9.9%
42190
 
8.0%
51469
 
5.4%
61145
 
4.2%
7767
 
2.8%
8586
 
2.2%
9412
 
1.5%
ValueCountFrequency (%)
461
< 0.1%
421
< 0.1%
411
< 0.1%
401
< 0.1%
361
< 0.1%
341
< 0.1%
331
< 0.1%
311
< 0.1%
302
< 0.1%
291
< 0.1%

Total_Claims
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct57
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1455.598245
Minimum8
Maximum5514
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size213.0 KiB
2022-07-24T11:37:36.893342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile96.3
Q1652
median1309
Q31541
95-th percentile3408
Maximum5514
Range5506
Interquartile range (IQR)889

Descriptive statistics

Standard deviation1224.028472
Coefficient of variation (CV)0.8409109283
Kurtosis3.550927794
Mean1455.598245
Median Absolute Deviation (MAD)358
Skewness1.831041934
Sum39654863
Variance1498245.7
MonotonicityNot monotonic
2022-07-24T11:37:37.123751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15415915
21.7%
13093947
14.5%
9513496
12.8%
3791810
 
6.6%
6521587
 
5.8%
55141337
 
4.9%
3408908
 
3.3%
485818
 
3.0%
3052697
 
2.6%
2455523
 
1.9%
Other values (47)6205
22.8%
ValueCountFrequency (%)
81
 
< 0.1%
95
 
< 0.1%
1197
 
0.4%
1465
 
0.2%
22141
 
0.5%
27199
0.7%
3926
 
0.1%
45438
1.6%
471
 
< 0.1%
69346
1.3%
ValueCountFrequency (%)
55141337
4.9%
3408908
3.3%
3318423
 
1.6%
3052697
2.6%
2508521
 
1.9%
2455523
 
1.9%
2429242
 
0.9%
2027153
 
0.6%
191789
 
0.3%
1659319
 
1.2%

Fracture
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct44
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144.9931726
Minimum1
Maximum339
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size213.0 KiB
2022-07-24T11:37:37.364890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q167
median180
Q3207
95-th percentile289
Maximum339
Range338
Interquartile range (IQR)140

Descriptive statistics

Standard deviation86.17112406
Coefficient of variation (CV)0.5943115978
Kurtosis-0.49210857
Mean144.9931726
Median Absolute Deviation (MAD)47
Skewness0.09728771286
Sum3950049
Variance7425.462621
MonotonicityNot monotonic
2022-07-24T11:37:37.594833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
2075915
21.7%
1803947
14.5%
1333496
12.8%
401786
 
6.6%
861676
 
6.2%
3391337
 
4.9%
203908
 
3.3%
35818
 
3.0%
289697
 
2.6%
7569
 
2.1%
Other values (34)6094
22.4%
ValueCountFrequency (%)
1102
 
0.4%
2406
1.5%
335
 
0.1%
5380
1.4%
638
 
0.1%
7569
2.1%
8329
1.2%
921
 
0.1%
10169
 
0.6%
1238
 
0.1%
ValueCountFrequency (%)
3391337
 
4.9%
289697
 
2.6%
2075915
21.7%
203908
 
3.3%
202423
 
1.6%
191523
 
1.9%
190153
 
0.6%
1803947
14.5%
164521
 
1.9%
1333496
12.8%

Low_Back
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct51
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219.2737951
Minimum2
Maximum820
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size213.0 KiB
2022-07-24T11:37:37.866966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile14
Q1109
median184
Q3220
95-th percentile515
Maximum820
Range818
Interquartile range (IQR)111

Descriptive statistics

Standard deviation184.3288804
Coefficient of variation (CV)0.8406334204
Kurtosis3.199336593
Mean219.2737951
Median Absolute Deviation (MAD)41
Skewness1.797868462
Sum5973676
Variance33977.13614
MonotonicityNot monotonic
2022-07-24T11:37:38.099430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2205915
21.7%
1843947
14.5%
1433496
12.8%
671786
 
6.6%
1091587
 
5.8%
8201337
 
4.9%
515908
 
3.3%
70818
 
3.0%
481697
 
2.6%
408523
 
1.9%
Other values (41)6229
22.9%
ValueCountFrequency (%)
2163
 
0.6%
3141
 
0.5%
4204
0.7%
5438
1.6%
826
 
0.1%
93
 
< 0.1%
11372
1.4%
121
 
< 0.1%
1312
 
< 0.1%
1434
 
0.1%
ValueCountFrequency (%)
8201337
4.9%
515908
3.3%
490242
 
0.9%
481697
2.6%
463423
 
1.6%
408523
 
1.9%
401521
 
1.9%
343153
 
0.6%
306319
 
1.2%
29989
 
0.3%

Shoulder
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct43
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.6569761
Minimum0
Maximum331
Zeros163
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size213.0 KiB
2022-07-24T11:37:38.329149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q145
median88
Q394
95-th percentile205
Maximum331
Range331
Interquartile range (IQR)49

Descriptive statistics

Standard deviation74.12469415
Coefficient of variation (CV)0.8360841685
Kurtosis3.233037589
Mean88.6569761
Median Absolute Deviation (MAD)34
Skewness1.725068858
Sum2415282
Variance5494.470283
MonotonicityNot monotonic
2022-07-24T11:37:38.532490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
945915
21.7%
883947
14.5%
223501
12.9%
543496
12.8%
3311337
 
4.9%
205908
 
3.3%
45818
 
3.0%
201697
 
2.6%
3579
 
2.1%
148523
 
1.9%
Other values (33)5522
20.3%
ValueCountFrequency (%)
0163
 
0.6%
165
 
0.2%
2199
 
0.7%
3579
2.1%
413
 
< 0.1%
5405
1.5%
63
 
< 0.1%
7182
 
0.7%
1060
 
0.2%
1189
 
0.3%
ValueCountFrequency (%)
3311337
4.9%
205908
3.3%
201697
2.6%
170242
 
0.9%
159423
 
1.6%
15089
 
0.3%
148523
 
1.9%
145153
 
0.6%
140521
 
1.9%
121319
 
1.2%

HIC_Total
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean452.9239438
Minimum3
Maximum1490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size213.0 KiB
2022-07-24T11:37:38.786490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile25
Q1217
median452
Q3521
95-th percentile971
Maximum1490
Range1487
Interquartile range (IQR)304

Descriptive statistics

Standard deviation331.2450586
Coefficient of variation (CV)0.7313480842
Kurtosis2.428709876
Mean452.9239438
Median Absolute Deviation (MAD)122
Skewness1.414642387
Sum12339007
Variance109723.2888
MonotonicityNot monotonic
2022-07-24T11:37:39.026588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5215915
21.7%
4523947
14.5%
3303496
12.8%
1291786
 
6.6%
2171587
 
5.8%
14901337
 
4.9%
923908
 
3.3%
150818
 
3.0%
971697
 
2.6%
747523
 
1.9%
Other values (43)6229
22.9%
ValueCountFrequency (%)
397
 
0.4%
466
 
0.2%
65
 
< 0.1%
8340
1.2%
1226
 
0.1%
15438
1.6%
191
 
< 0.1%
2034
 
0.1%
21346
1.3%
223
 
< 0.1%
ValueCountFrequency (%)
14901337
 
4.9%
971697
 
2.6%
923908
 
3.3%
824423
 
1.6%
747523
 
1.9%
737242
 
0.9%
705521
 
1.9%
678153
 
0.6%
53589
 
0.3%
5215915
21.7%

HIC_Rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct21
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3250559777
Minimum0.19
Maximum0.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size213.0 KiB
2022-07-24T11:37:39.190241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.19
5-th percentile0.27
Q10.31
median0.34
Q30.35
95-th percentile0.35
Maximum0.67
Range0.48
Interquartile range (IQR)0.04

Descriptive statistics

Standard deviation0.02924369684
Coefficient of variation (CV)0.08996511016
Kurtosis4.203522498
Mean0.3250559777
Median Absolute Deviation (MAD)0.01
Skewness-0.8745670506
Sum8855.5
Variance0.0008551938049
MonotonicityNot monotonic
2022-07-24T11:37:39.382535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0.347701
28.3%
0.357487
27.5%
0.333025
 
11.1%
0.272443
 
9.0%
0.31987
 
7.3%
0.311389
 
5.1%
0.28942
 
3.5%
0.32814
 
3.0%
0.29720
 
2.6%
0.25439
 
1.6%
Other values (11)296
 
1.1%
ValueCountFrequency (%)
0.1934
 
0.1%
0.238
 
0.1%
0.2238
 
0.1%
0.236
 
< 0.1%
0.25439
 
1.6%
0.2612
 
< 0.1%
0.272443
9.0%
0.28942
 
3.5%
0.29720
 
2.6%
0.31987
7.3%
ValueCountFrequency (%)
0.675
 
< 0.1%
0.51
 
< 0.1%
0.41
 
< 0.1%
0.384
 
< 0.1%
0.3716
 
0.1%
0.36141
 
0.5%
0.357487
27.5%
0.347701
28.3%
0.333025
 
11.1%
0.32814
 
3.0%

Emp_15plus
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean907.3963036
Minimum29.5
Maximum3400.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size213.0 KiB
2022-07-24T11:37:39.542213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum29.5
5-th percentile298.8
Q1540
median540
Q3540
95-th percentile3400.8
Maximum3400.8
Range3371.3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation987.5022504
Coefficient of variation (CV)1.088281103
Kurtosis2.436683835
Mean907.3963036
Median Absolute Deviation (MAD)0
Skewness2.055902782
Sum24720197.5
Variance975160.6946
MonotonicityNot monotonic
2022-07-24T11:37:39.677155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
54016731
61.4%
3400.83611
 
13.3%
298.81662
 
6.1%
761.51628
 
6.0%
448.91121
 
4.1%
29.5979
 
3.6%
900.5818
 
3.0%
395.7279
 
1.0%
373168
 
0.6%
1547.4118
 
0.4%
Other values (2)128
 
0.5%
ValueCountFrequency (%)
29.5979
 
3.6%
55.651
 
0.2%
298.81662
 
6.1%
373168
 
0.6%
395.7279
 
1.0%
448.91121
 
4.1%
54016731
61.4%
540.877
 
0.3%
761.51628
 
6.0%
900.5818
 
3.0%
ValueCountFrequency (%)
3400.83611
 
13.3%
1547.4118
 
0.4%
900.5818
 
3.0%
761.51628
 
6.0%
540.877
 
0.3%
54016731
61.4%
448.91121
 
4.1%
395.7279
 
1.0%
373168
 
0.6%
298.81662
 
6.1%

Emp_15.24
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141.5604669
Minimum1.9
Maximum670.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size213.0 KiB
2022-07-24T11:37:39.870376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile26.2
Q158.5
median58.5
Q362.5
95-th percentile670.8
Maximum670.8
Range668.9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation209.196128
Coefficient of variation (CV)1.477786366
Kurtosis2.46234693
Mean141.5604669
Median Absolute Deviation (MAD)0
Skewness2.07985624
Sum3856531.8
Variance43763.01998
MonotonicityNot monotonic
2022-07-24T11:37:40.004463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
58.516731
61.4%
670.83611
 
13.3%
26.21662
 
6.1%
62.51628
 
6.0%
190.81121
 
4.1%
1.9979
 
3.6%
74.5818
 
3.0%
26.8279
 
1.0%
24.4168
 
0.6%
149.6118
 
0.4%
Other values (2)128
 
0.5%
ValueCountFrequency (%)
1.9979
 
3.6%
3.551
 
0.2%
24.4168
 
0.6%
26.21662
 
6.1%
26.8279
 
1.0%
53.577
 
0.3%
58.516731
61.4%
62.51628
 
6.0%
74.5818
 
3.0%
149.6118
 
0.4%
ValueCountFrequency (%)
670.83611
 
13.3%
190.81121
 
4.1%
149.6118
 
0.4%
74.5818
 
3.0%
62.51628
 
6.0%
58.516731
61.4%
53.577
 
0.3%
26.8279
 
1.0%
26.21662
 
6.1%
24.4168
 
0.6%

EmpPer_1524
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.71043277
Minimum6.29
Maximum42.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size213.0 KiB
2022-07-24T11:37:40.171463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6.29
5-th percentile6.77
Q110.83
median10.83
Q310.83
95-th percentile19.72
Maximum42.5
Range36.21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.078146848
Coefficient of variation (CV)0.5568769353
Kurtosis10.09620452
Mean12.71043277
Median Absolute Deviation (MAD)0
Skewness3.114717461
Sum346270.32
Variance50.10016281
MonotonicityNot monotonic
2022-07-24T11:37:40.276271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10.8316731
61.4%
19.723611
 
13.3%
8.771662
 
6.1%
8.211628
 
6.0%
42.51121
 
4.1%
6.44979
 
3.6%
8.27818
 
3.0%
6.77279
 
1.0%
6.54168
 
0.6%
9.67118
 
0.4%
Other values (2)128
 
0.5%
ValueCountFrequency (%)
6.2951
 
0.2%
6.44979
 
3.6%
6.54168
 
0.6%
6.77279
 
1.0%
8.211628
 
6.0%
8.27818
 
3.0%
8.771662
 
6.1%
9.67118
 
0.4%
9.8977
 
0.3%
10.8316731
61.4%
ValueCountFrequency (%)
42.51121
 
4.1%
19.723611
 
13.3%
10.8316731
61.4%
9.8977
 
0.3%
9.67118
 
0.4%
8.771662
 
6.1%
8.27818
 
3.0%
8.211628
 
6.0%
6.77279
 
1.0%
6.54168
 
0.6%

Emp_25.54
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean574.2859377
Minimum22.1
Maximum2048.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size213.0 KiB
2022-07-24T11:37:40.482903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum22.1
5-th percentile200.4
Q1362.5
median362.5
Q3362.5
95-th percentile2048.9
Maximum2048.9
Range2026.8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation586.7789897
Coefficient of variation (CV)1.021754062
Kurtosis2.346646996
Mean574.2859377
Median Absolute Deviation (MAD)0
Skewness2.016822919
Sum15645271.8
Variance344309.5828
MonotonicityNot monotonic
2022-07-24T11:37:40.693117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
362.516731
61.4%
2048.93611
 
13.3%
200.41662
 
6.1%
497.51628
 
6.0%
205.41121
 
4.1%
22.1979
 
3.6%
633.5818
 
3.0%
263.6279
 
1.0%
274.2168
 
0.6%
1002.7118
 
0.4%
Other values (2)128
 
0.5%
ValueCountFrequency (%)
22.1979
 
3.6%
4351
 
0.2%
200.41662
 
6.1%
205.41121
 
4.1%
263.6279
 
1.0%
274.2168
 
0.6%
362.516731
61.4%
370.177
 
0.3%
497.51628
 
6.0%
633.5818
 
3.0%
ValueCountFrequency (%)
2048.93611
 
13.3%
1002.7118
 
0.4%
633.5818
 
3.0%
497.51628
 
6.0%
370.177
 
0.3%
362.516731
61.4%
274.2168
 
0.6%
263.6279
 
1.0%
205.41121
 
4.1%
200.41662
 
6.1%

EmpPer_2554
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.6509775
Minimum45.76
Maximum77.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size213.0 KiB
2022-07-24T11:37:40.906381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum45.76
5-th percentile60.25
Q167.07
median67.13
Q367.13
95-th percentile70.35
Maximum77.34
Range31.58
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation5.103139115
Coefficient of variation (CV)0.07773135008
Kurtosis7.17034357
Mean65.6509775
Median Absolute Deviation (MAD)0
Skewness-2.293555519
Sum1788529.58
Variance26.04202883
MonotonicityNot monotonic
2022-07-24T11:37:41.075122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
67.1316731
61.4%
60.253611
 
13.3%
67.071662
 
6.1%
65.331628
 
6.0%
45.761121
 
4.1%
74.92979
 
3.6%
70.35818
 
3.0%
66.62279
 
1.0%
73.51168
 
0.6%
64.8118
 
0.4%
Other values (2)128
 
0.5%
ValueCountFrequency (%)
45.761121
 
4.1%
60.253611
 
13.3%
64.8118
 
0.4%
65.331628
 
6.0%
66.62279
 
1.0%
67.071662
 
6.1%
67.1316731
61.4%
68.4477
 
0.3%
70.35818
 
3.0%
73.51168
 
0.6%
ValueCountFrequency (%)
77.3451
 
0.2%
74.92979
 
3.6%
73.51168
 
0.6%
70.35818
 
3.0%
68.4477
 
0.3%
67.1316731
61.4%
67.071662
 
6.1%
66.62279
 
1.0%
65.331628
 
6.0%
64.8118
 
0.4%

Emp_55plus
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean191.5453841
Minimum5.4
Maximum681.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size213.0 KiB
2022-07-24T11:37:41.231501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5.4
5-th percentile52.6
Q1119
median119
Q3119
95-th percentile681.1
Maximum681.1
Range675.7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation195.7759214
Coefficient of variation (CV)1.022086344
Kurtosis2.250821843
Mean191.5453841
Median Absolute Deviation (MAD)0
Skewness1.982974251
Sum5218270.9
Variance38328.21138
MonotonicityNot monotonic
2022-07-24T11:37:41.421120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
11916731
61.4%
681.13611
 
13.3%
72.31662
 
6.1%
201.61628
 
6.0%
52.61121
 
4.1%
5.4979
 
3.6%
192.5818
 
3.0%
105.2279
 
1.0%
74.5168
 
0.6%
393.1118
 
0.4%
Other values (2)128
 
0.5%
ValueCountFrequency (%)
5.4979
 
3.6%
9.251
 
0.2%
52.61121
 
4.1%
72.31662
 
6.1%
74.5168
 
0.6%
105.2279
 
1.0%
117.277
 
0.3%
11916731
61.4%
192.5818
 
3.0%
201.61628
 
6.0%
ValueCountFrequency (%)
681.13611
 
13.3%
393.1118
 
0.4%
201.61628
 
6.0%
192.5818
 
3.0%
11916731
61.4%
117.277
 
0.3%
105.2279
 
1.0%
74.5168
 
0.6%
72.31662
 
6.1%
52.61121
 
4.1%

EmpPer_55plus
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.62863818
Minimum11.72
Maximum26.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size213.0 KiB
2022-07-24T11:37:41.618221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum11.72
5-th percentile18.31
Q121.67
median22.04
Q322.04
95-th percentile26.47
Maximum26.59
Range14.87
Interquartile range (IQR)0.37

Descriptive statistics

Standard deviation2.657727615
Coefficient of variation (CV)0.1228800257
Kurtosis5.936274535
Mean21.62863818
Median Absolute Deviation (MAD)0
Skewness-1.748207059
Sum589228.99
Variance7.063516074
MonotonicityNot monotonic
2022-07-24T11:37:41.791264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
22.0416731
61.4%
20.033611
 
13.3%
24.21662
 
6.1%
26.471628
 
6.0%
11.721121
 
4.1%
18.31979
 
3.6%
21.38818
 
3.0%
26.59279
 
1.0%
19.97168
 
0.6%
25.4118
 
0.4%
Other values (2)128
 
0.5%
ValueCountFrequency (%)
11.721121
 
4.1%
16.5551
 
0.2%
18.31979
 
3.6%
19.97168
 
0.6%
20.033611
 
13.3%
21.38818
 
3.0%
21.6777
 
0.3%
22.0416731
61.4%
24.21662
 
6.1%
25.4118
 
0.4%
ValueCountFrequency (%)
26.59279
 
1.0%
26.471628
 
6.0%
25.4118
 
0.4%
24.21662
 
6.1%
22.0416731
61.4%
21.6777
 
0.3%
21.38818
 
3.0%
20.033611
 
13.3%
19.97168
 
0.6%
18.31979
 
3.6%

Interactions

2022-07-24T11:37:25.889636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:14.378093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:19.025826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:23.722858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:28.235305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:32.397129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:36.133378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:40.423772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:44.559340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:48.740592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:52.781765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:56.889935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:01.043409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:05.439969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:09.671769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:13.866357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:17.682539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:21.586692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:26.078457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:14.636950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:19.259496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:23.984074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:28.443732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:32.669398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:36.354687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:40.674527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:44.745209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:48.968929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:52.991480image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:57.093711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:01.287557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:05.633604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:09.859389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:14.139011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:17.904690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:21.759916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:26.322995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:15.310164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:19.513800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:24.242364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:28.679756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:32.893802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:36.593077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:40.925836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:44.909992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:49.132805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:53.180619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:57.294716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:01.539505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:05.825158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:10.097577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:14.276348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:18.134724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:21.920100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:26.561167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:15.553847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:19.729254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:24.451722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:28.900366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:33.080766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:36.779647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:41.154506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:45.178062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:49.393025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:53.402506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:57.491605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:01.785864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:06.006570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:10.353745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:14.492624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:18.373270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-07-24T11:36:22.950397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:27.507492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:31.790991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:35.419577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:39.479290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:43.961323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:47.986682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:52.047102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:56.185850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:00.362200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:04.707678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:08.961466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:13.136766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:16.909443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:20.921483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:25.228909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:29.655633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:18.623212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:23.193493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:27.759219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:32.029540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:35.632939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:39.687726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:44.105581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:48.208632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:52.278551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:56.406665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:00.574551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:04.972185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:09.180612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:13.384969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:17.154922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:21.104752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:25.467045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:29.859961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:18.817694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:23.435902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:28.016362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:32.190492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:35.875690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:39.924633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:44.314567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:48.469599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:52.522051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:36:56.655549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:00.816534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:05.174652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:09.405142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:13.606381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:17.427393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:21.333281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-24T11:37:25.682917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-07-24T11:37:42.580729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-24T11:37:42.811327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-07-24T11:37:43.188280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-24T11:37:43.518702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-07-24T11:37:43.740110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-07-24T11:37:30.223129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-07-24T11:37:31.683171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

FV_DFV_MFV_DATEVISIT_CODEWP_IDWP_NAMEWP_ADDRESSREGION_CODEIND_NameIND_CatINDNAICSNAICS.DESCRCONTRA_TotalTotal_ClaimsFractureLow_BackShoulderHIC_TotalHIC_RateEmp_15plusEmp_15.24EmpPer_1524Emp_25.54EmpPer_2554Emp_55plusEmpPer_55plus
023102019-10-232019-10-23 21239052123905Solutions CMD275 Daniel Street, Arnprior - Renfrew - Arnprior, ON, CANADA A0A 1Z0KConstructionGoods-producing23236210Industrial Building and Structure Construction2951133143543300.35540.058.510.83362.567.13119.022.04
1812019-01-082019-01-08 20124822012482D S CONSTRUCTIONListowel - Perth - North Perth, ON, CANADA LOT 17, 12, 14, 16, 29, 30 off of Kincard StKConstructionGoods-producing23236110Residential Building Construction1951133143543300.35540.058.510.83362.567.13119.022.04
21612019-01-162019-01-16 20557082055708Foresterie Malloy Inc132 bisson Rang, Sherbrooke Township - Haldimand-Norfolk - Haldimand County, QC, CANADA J0V 1Y0KAgriculture, forestry, fishing and huntingGoods-producing11113312Contract Logging0208282915720.351547.4149.69.671002.764.80393.125.40
32312019-01-232019-01-23 20565302056530DEBUS DESIGN BUILD199, Stratford - Perth - Stratford, ON, CANADA 199 CAMBRIA STREETKConstructionGoods-producing23236110Residential Building Construction6951133143543300.35540.058.510.83362.567.13119.022.04
4422019-02-042019-02-04 20584582058458LANG CONTRACTINGSt. Marys - Perth - St. Marys, ON, CANADA 74 WILSON CRTKConstructionGoods-producing23236110Residential Building Construction5951133143543300.35540.058.510.83362.567.13119.022.04
51322019-02-132019-02-13 20611772061177ALEXANDER HOLMANShakespeare - Perth - Perth East, ON, CANADA LOT 3 WILSON STREETKConstructionGoods-producing23236110Residential Building Construction7951133143543300.35540.058.510.83362.567.13119.022.04
61132019-03-112019-03-11 20662692066269CEDAR ROSE HOMESMilverton - Perth - Perth East, ON, CANADA LOT 42 REAGANKConstructionGoods-producing23236110Residential Building Construction5951133143543300.35540.058.510.83362.567.13119.022.04
7142019-04-012019-04-01 20729202072920STROH CUSTOM H0MESMilverton - Perth - Perth East, ON, CANADA LOT 47 REAGAN STREETKConstructionGoods-producing23236110Residential Building Construction2951133143543300.35540.058.510.83362.567.13119.022.04
8462019-06-042019-06-04 20870912087091KEN KRANTZ3123, Stratford - Perth - Stratford, ON, CANADA 3123 VIVIAN LINE 37KConstructionGoods-producing23236110Residential Building Construction3951133143543300.35540.058.510.83362.567.13119.022.04
9462019-06-042019-06-04 20871392087139CEDAR ROSE HOMESMilverton - Perth - Perth East, ON, CANADA Lot 5 Yost CresecentKConstructionGoods-producing23236110Residential Building Construction5951133143543300.35540.058.510.83362.567.13119.022.04

Last rows

FV_DFV_MFV_DATEVISIT_CODEWP_IDWP_NAMEWP_ADDRESSREGION_CODEIND_NameIND_CatINDNAICSNAICS.DESCRCONTRA_TotalTotal_ClaimsFractureLow_BackShoulderHIC_TotalHIC_RateEmp_15plusEmp_15.24EmpPer_1524Emp_25.54EmpPer_2554Emp_55plusEmpPer_55plus
272331192019-09-112019-09-11 21098452109845JARNEL CONTRACTING LTDKenora - Kenora - Kenora, ON, CANADA P9N 4E6PConstructionGoods-producing23236110Residential Building Construction13794067221290.34540.058.510.83362.567.13119.022.04
2723424112019-11-242019-11-24 11167431116743WALMART CANADA24 MIIKANA Way, Kenora - Kenora - Kenora, ON, CANADA P9N 4J1PServiceService-producingMultiple452110Department Stores481564124472350.293400.8670.819.722048.960.25681.120.03
272356112019-11-062019-11-06 6897468974BIRCHWOOD TERRACE237 LAKEVIEW Drive, Kenora - Kenora - Kenora, ON, CANADA P9N 4J7PHealth care and social assistanceService-producing62623310Community Care Facilities for the Elderly083124164502380.29900.574.58.27633.570.35192.521.38
27236642019-04-062019-04-06 19285931928593Emcon Services20 B ANDERSON Road, Kenora - Kenora - Kenora, ON, CANADA P9N 4J8PConstructionGoods-producing23237310Highway, Street and Bridge Construction23794067221290.34540.058.510.83362.567.13119.022.04
2723724102019-10-242019-10-24 20972402097240LMD CONTRACTING 2008 LIMITEDKenora - Kenora - Kenora, ON, CANADA P9N 4K9PConstructionGoods-producing23236220Commercial and Institutional Building Construction13794067221290.34540.058.510.83362.567.13119.022.04
272383132019-03-312019-03-31 20729632072963CR Construction145 Rabbit Lake Road, Kenora - Kenora - Kenora, ON, CANADA P9N 4L1PConstructionGoods-producing23238990All Other Specialty Trade Contractors43794067221290.34540.058.510.83362.567.13119.022.04
272392022019-02-202019-02-20 12578771257877CITY OF KENORA PUBLIC WORKS BUILDING60 FOURTEENTH Street North, Kenora - Kenora - Kenora, ON, CANADA P9N 4M9PConstructionGoods-producing23236110Residential Building Construction03794067221290.34540.058.510.83362.567.13119.022.04
27240442019-04-042019-04-04 12578771257877CITY OF KENORA PUBLIC WORKS BUILDING60 FOURTEENTH Street North, Kenora - Kenora - Kenora, ON, CANADA P9N 4M9PConstructionGoods-producing23236110Residential Building Construction13794067221290.34540.058.510.83362.567.13119.022.04
272411052019-05-102019-05-10 12578771257877CITY OF KENORA PUBLIC WORKS BUILDING60 FOURTEENTH Street North, Kenora - Kenora - Kenora, ON, CANADA P9N 4M9PConstructionGoods-producing23236110Residential Building Construction13794067221290.34540.058.510.83362.567.13119.022.04
27242722019-02-072019-02-07 18135571813557Neil Fregeau Construction Ltd16 C Storm Bay Rd, (#60 Fire Rd) Road, Haycock Township - Kenora - Kenora, Unorganized, ON, CANADA P9N 9Z1PConstructionGoods-producing23236110Residential Building Construction13794067221290.34540.058.510.83362.567.13119.022.04